Prior to training our model, we center all parameters \(x_i\)  in our training data (both the targets and input magnitudes) by subtracting their mean: \(x_{i,t}=x_i - {\rm E}(x_i)\)  E stands for mathematical expectation.. This normalization reduces the upper and lower bound of the parameter distribution, and makes them all zero-mean distributed, which can accelerate the training speed [1]}.
